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46 results on '"Lefort, Gaëlle"'

1. Deciphering autism heterogeneity: a molecular stratification approach in four mouse models

Author

Gora, Caroline, Dudas, Ana, Vaugrente, Océane, Drobecq, Lucile, Pecnard, Emmanuel, Lefort, Gaëlle, and Pellissier, Lucie P.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction, communication, as well as restrained or stereotyped behaviors. The inherent heterogeneity within the autism spectrum poses challenges for developing effective pharmacological treatments targeting core features. Successful clinical trials require the identification of robust markers to enable patient stratification. In this study, we explored molecular markers within the oxytocin and immediate early gene families across five interconnected brain structures of the social circuit in four distinct ASD mouse models, each exhibiting unique behavioral features along the autism spectrum. While dysregulations in the oxytocin family were model-specific, immediate early genes displayed widespread alterations, reflecting global changes in social plasticity. Through integrative analysis, we identified Egr1, Foxp1, Homer1a, Oxt and Oxtr as five robust and discriminant molecular markers facilitating successful stratification of the four models. Importantly, our stratification demonstrated predictive values when challenged with a fifth mouse model or identifying subgroups of mice potentially responsive to oxytocin treatment. Beyond providing insights into oxytocin and immediate early gene mRNA dynamics, this proof-of-concept study represents a significant step toward potential stratification of individuals with ASD. The implications extend to enhancing the success of clinical trials and guiding personalized medicine for distinct subgroups of individuals with autism.

Published
2024

2. Effect of the social environment on olfaction and social skills in WT and mouse model of autism

Author

Gora, Caroline, Dudas, Ana, Court, Lucas, Annamneedi, Anil, Lefort, Gaëlle, Picoreti-Nakahara, Thiago-Seike, Azzopardi, Nicolas, Acquistapace, Adrien, Lainé, Anne-Lyse, Trouillet, Anne-Charlotte, Drobecq, Lucile, Pecnard, Emmanuel, Piegu, Benoit, Crépieux, Pascale, Chamero, Pablo, and Pellissier, Lucie P.

Subjects
Quantitative Biology - Neurons and Cognition, Physics - Physics and Society
Abstract

Autism spectrum disorders are complex, polygenic and heterogenous neurodevelopmental conditions, imposing a substantial economic burden. Genetics are influenced by the environment, specifically the social experience during the critical neurodevelopmental period. Despite efficacy of early behavior interventions targeted specific behaviors in some autistic children, there is no sustainable treatment for the two core symptoms: deficits in social interaction and communication, and stereotyped or restrained behaviors or interests. In this study, we investigated the impact of the social environment on both wild-type (WT) and Shank3 knockout (KO) mice, a mouse model that reproduces core autism-like symptoms. Our findings revealed that WT mice raised in an enriched social environment maintained social interest towards new conspecifics across multiple trials. Additionally, we observed that 2 hours or chronic social isolation induced social deficits or enhanced social interaction and olfactory neuron responses in WT animals, respectively. Notably, chronic social isolation restored both social novelty and olfactory deficits, and normalized self-grooming behavior in Shank3 KO mice. These results novel insights for the implementation of behavioral intervention and inclusive classrooms programs for children with ASD.

Published
2023

4. Chronic dietary exposure to a glyphosate-based herbicide alters ovarian functions in young female broilers

Author

Freville Mathias, Estienne Anthony, Ramé Christelle, Lefort Gaëlle, Piégu Benoît, Chahnamian Marine, Froment Pascal, and Dupont Joëlle

Subjects
glyphosate, young broiler, food intake, ovary, transcriptomic approach, Animal culture, SF1-1100
Abstract

ABSTRACT: Glyphosate (GLY)-based herbicide (GBH) formulations are widely used pesticides in agriculture. The European Union recently decided to extend the use of GLY in Europe until 2034. Previously, we showed that chronic dietary GBH exposure in adult hens resulted in a reversible increase in early mortality in chicken embryos. In this present study, we investigated the GBH effects on metabolism and ovarian functions by using a transcriptomic approach in vivo in young female broilers and in vitro in ovarian explant cultures. We exposed 11-day-old female broilers to 13 mg GLY equivalent/kg body weight/d (GBH13, n = 20), 34 mg GLY equivalent/kg body weight/d (GBH34, n = 20), or a standard diet (control [CT], n = 20) for 25 d. These 2 GBH concentrations correspond to approximatively one-eighth and one-third of the no observed adverse effect level (NOAEL) as defined by European Food Safety Authority in birds. During this period, we evaluated body weight, fattening, food intake, and the weight of organs (including the ovaries). Chronic dietary GBH exposure dose dependently reduced food intake, body weight, and fattening, but increased oxidative stress and relative ovary weight. We analyzed the ovarian gene expression profile in CT, GBH13, and GBH34 broilers with RNA sequencing and showed that differentially expressed genes are mainly enriched in pathways related to cholesterol metabolism, steroidogenesis, and RNA processing. With quantitative polymerase chain reaction and western blotting, we confirmed that GBH decreased ovarian STAR and CYP19A1 messenger RNA and protein expression, respectively. Furthermore, we confirmed that GBH altered steroid production in ovarian explants. We have identified potential regulatory networks associated with GBH. These data provide valuable support for understanding the ovarian transcriptional regulatory mechanism of GBH in growing broilers.

Published
2024
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10. Effect of the social environment on olfaction and social skills in WT and a mouse model of autism

Author

Pellissier, Lucie, primary, Gora, Caroline, additional, Dudas, Ana, additional, Court, Lucas, additional, Annamneedi, Anil, additional, Lefort, Gaëlle, additional, Nakahara, Thiago, additional, Azzopardi, Nicolas, additional, Acquistapace, Adrien, additional, Laine, Anne-Lyse, additional, Trouillet, Anne-Charlotte, additional, Drobecq, Lucile, additional, Pecnard, Emmanuel, additional, Piégu, Benoît, additional, Crepieux, Pascale, additional, and Chamero, Pablo, additional

Published
2024
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11. Facial blushing and feather fluffing are indicators of emotions in domestic fowl (Gallus gallus domesticus).

Author

Arnould, Cécile, Love, Scott A., Piégu, Benoît, Lefort, Gaëlle, Blache, Marie-Claire, Parias, Céline, Soulet, Delphine, Lévy, Frédéric, Nowak, Raymond, Lansade, Léa, and Bertin, Aline

Subjects
EMOTIONS, FEATHERS, FACIAL expression, FOWLING, HENS
Abstract

The study of facial expressions in mammals provided great advances in the identification of their emotions and then in the comprehension of their sentience. So far, this area of research has excluded birds. With a naturalist approach, we analysed facial blushing and feather displays in domestic fowl. Hens were filmed in situations contrasting in emotional valence and arousal level: situations known to indicate calm states (positive valence / low arousal), have rewarding effects (positive valence / high arousal) or induce fear-related behaviour (negative valence / high arousal). Head feather position as well as skin redness of comb, wattles, ear lobes and cheeks varied across these situations. Skin of all four areas was less red in situations with low arousal compared to situations with higher arousal. Furthermore, skin redness of the cheeks and ear lobes also varied depending on the valence of the situation: redness was higher in situations with negative valence compared to situations with positive valence. Feather position also varied with the situations. Feather fluffing was mostly observed in positively valenced situations, except when hens were eating. We conclude that hens have facial displays that reveal their emotions and that blushing is not exclusive to humans. This opens a promising way to explore the emotional lives of birds, which is a critical step when trying to improve poultry welfare. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Chronic dietary exposure to a glyphosate-based herbicide results in total or partial reversibility of plasma oxidative stress, cecal microbiota abundance and short-chain fatty acid composition in broiler hens

Author

Fréville, Mathias, primary, Estienne, Anthony, additional, Ramé, Christelle, additional, Lefort, Gaëlle, additional, Chahnamian, Marine, additional, Staub, Christophe, additional, Venturi, Eric, additional, Lemarchand, Julie, additional, Maximin, Elise, additional, Hondelatte, Alice, additional, Zemb, Olivier, additional, Canlet, Cécile, additional, Guabiraba, Rodrigo, additional, Froment, Pascal, additional, and Dupont, Joëlle, additional

Published
2022
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16. Plasma 1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake.

Author

Liaubet, Laurence, Guilmineau, Camille, Lefort, Gaëlle, Billon, Yvon, Reigner, Sébastien, Bailly, Jean, Marty-Gasset, Nathalie, Gress, Laure, Servien, Rémi, Bonnet, Agnès, Gilbert, Hélène, Vialaneix, Nathalie, and Quesnel, Hélène

Subjects
PIGLETS, AMINO acids, UREA, SWINE breeding, NEWBORN infants, ENERGY metabolism
Abstract

Together with environmental factors, physiological maturity at birth is a major determinant for neonatal survival and postnatal development in mammalian species. Maturity at birth is the outcome of complex mechanisms of intra-uterine development and maturation during the end of gestation. In pig production, piglet preweaning mortality averages 20% of the litter and thus, maturity is a major welfare and economic concern. Here, we used both targeted and untargeted metabolomic approaches to provide a deeper understanding of the maturity in a model of lines of pigs divergently selected on residual feed intake (RFI), previously shown to have contrasted signs of maturity at birth. Analyses were conducted on plasma metabolome of piglets at birth and integrated with other phenotypic characteristics associated to maturity. We confirmed proline and myo-inositol, previously described for their association with delayed growth, as potential markers of maturity. Urea cycle and energy metabolism were found more regulated in piglets from high and low RFI lines, respectively, suggesting a better thermoregulation ability for the low RFI (with higher feed efficiency) piglets. [ABSTRACT FROM AUTHOR]

Published
2023
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17. Characterization of human isogenic epithelial cell lines as a relevant tool to study colon carcinogenesis and interaction between genes and environment

Author

Tête, Arnaud, Arnaud, Liana C., Le Mentec, Hélène, Gallais, Isabelle, Poupin, Nathalie, Tournadre, Noémie, Duarte-Hospital, Carolina, Lippi, Yannick, Mathevet, Fanny, Lefort, Gaëlle, Burel, Agnes, Surya, R., Boutet-Robinet, Elisa, Shay, Jerry W., Vialaneix, Nathalie, Bortoli, Sylvie, Lagadic-Gossmann, Dominique, Huc, Laurence, Vialaneix, Nathalie, Toxicité environnementale, cibles thérapeutiques, signalisation cellulaire (T3S - UMR_S 1124), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Contaminants & Stress Cellulaire (ToxAlim-COMICS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), EHESP-Irset (EHESP-Irset), École des Hautes Études en Santé Publique [EHESP] (EHESP), Métabolisme et Xénobiotiques (ToxAlim-MeX), Université Paris Descartes, Sorbonne Paris Cité, Transcriptomic impact of Xenobiotics (E23 TRiX), Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biosit : biologie, santé, innovation technologique (SFR UMS CNRS 3480 - INSERM 018), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Bina Nusantara University [Jakarta], University of Texas Southwestern Medical Center, and ITMO Cancer, METAhCOL

Subjects
[SDV.CAN] Life Sciences [q-bio]/Cancer, [SDV.CAN]Life Sciences [q-bio]/Cancer, digestive system diseases
Abstract

International audience; Colorectal cancer (CRC) is the fourth most common cause of death from cancer worldwide. CRC is a multistep and progressive disease where genetic factors are important in the initiation, the development and the progression of the disease. CRC can arise from sequential steps including the acquisition of mutations in the adenomatous polyposis coli (APC), followed by the mutational activation of oncogene KRAS and the inactivation of the tumor suppressor gene, TP53. The occurrence of colorectal cancer is largely influenced by the environment, including food contaminants, lifestyle and nutrition. However, the influence of mutations on the response to environmental pollutants is poorly evaluated. Environmental carcinogenesis lacks robust models to explore the interaction between genes and environment and to determine whether genetic mutations associated with colon carcinogenesis generate a particular susceptibility to the harmful effects of pollutants. Here, we outline the characterization of an innovative cellular model that consists in 6 human isogenic epithelial colon cell lines bearing mutations on driver genes of CRC. Altogether, these cell lines recapitulate colon carcinogenesis from the healthy, preneoplastic, adenoma and carcinoma stages in a simplified way. We aim to perform a phenotypic and metabolic description of these cell lines, with a particular focus on their ability to express the battery of detoxification enzymes, to have a differential sensitivity to genotoxicity and cell death stimuli, to display a flexible energy metabolism, to migrate and grow without anchorage. We show that these models of human isogenic colonic cells of CRC could be a powerful and relevant tool to study the effects of environmental pollutants on the colorectal carcinogenesis from the early to the late and metastatic stages and to evaluate gene-environment interactions in food toxicology.

Published
2022

18. Quantification automatique de métabolites dans un spectre RMN et application à la description de la maturité périnatale chez le porc

Author

Lefort, Gaëlle and Lefort, Gaëlle

Subjects
[SDV] Life Sciences [q-bio], Variable selection, Résonance magnétique nucléaire, Nuclear Magnetic Resonance, Spectrum alignment, Metabolomics, Multiple testing, Tests multiples, Sélection de variables, Alignement de spectres, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Métabolomique
Abstract

Among all the omics data which characterize the biological functioning of an organism, metabolomics is a promising approach in systems biology for phenotype characterization or biomarker discovery. 1H Nuclear Magnetic Resonance (NMR) is a high-throughput technology that allows to obtain characteristic spectra of a complex mixture of metabolites present in a sample of interest. However, the analysis of 1H NMR spectra remains difficult, mainly due to the fact that the measure of different amounts of metabolites present in the sample is indirect with this technique.A promising approach for the analysis of such data is to identify and quantify metabolites present in the complex mixture from its spectrum and to perform the statistical analysis on the results of this quantification. A first part of this thesis consists in improving an existing method of metabolite quantification, named ASICS, and in its implementation in an R /Bioconductor package. A new method that integrates information obtained from several complex spectra of the same experiment during the quantification process is also proposed in order to improve the reliability of the results.A second part of this thesis concerns the application of this method to a problem of neonatal mortality in piglets and more precisely the description of the mechanisms involved in the establishment of maturity. Analysis of NMR spectra of plasma, urine and amniotic fluid from late gestation fetuses allow to identify metabolic pathways involving numerous amino acids and sugars (growth and energy supply) as well as glutathione metabolism (oxidative stress)., Parmi les nombreuses données omiques qui décrivent le fonctionnement biologique d'un organisme, le métabolome suscite un intérêt croissant car il est plus proche des phénotypes d'intérêt et qu'il a donc avoir un potentiel important pour la recherche de biomarqueurs. La spectrométrie par résonance magnétique nucléaire (RMN) est une technologie haut-débit qui produit des spectres caractéristiques du mélange complexe de métabolites présents dans un échantillon d'intérêt. Cependant, leur interprétation biologique est difficile car ceux-ci ne donnent pas une mesure explicite des différentes quantités de métabolites présents dans l'échantillon.Une approche prometteuse pour l'analyse de ces données consiste à identifier et quantifier les métabolites présents dans le mélange complexe à partir de son spectre et à réaliser l'analyse statistique sur les résultats de cette quantification. Une première partie de cette thèse a consisté en l'amélioration d'une méthode de quantification existante, ASICS, ainsi qu'à son implémentation dans un package R/Bioconductor. Une nouvelle méthode, prenant en compte l'ensemble des spectres d'une expérience lors de la quantification, a aussi été proposée dans le but d'améliorer la fiabilité des résultats.Un second volet de cette thèse concerne l'application de cette méthode au problème de mortalité néonatale des porcelets et plus précisément à la description des mécanismes impliqués dans la mise en place de la maturité. L'analyse des spectres RMN de plasma, d'urine et de liquide amniotique de foetus en fin de gestation a permis d'identifier des voies métaboliques impliquant de nombreux acides aminés et sucres (croissance et apport d'énergie) ainsi que le métabolisme du glutathion (stress oxydatif).

Published
2021

20. Characterization of human isogenic epithelial cell lines as a relevant tool to study colon carcinogenesis and interaction between genes and environment

Author

Tête, Arnaud, Arnaud, Liana C., Le Mentec, Hélène, Gallais, Isabelle, Tournadre, Noémie, Duarte-Hospital, Carolina, Lippi, Yannick, Mathevet, Fanny, Lefort, Gaëlle, Burel, Agnès, Surya, R., Boutet-Robinet, Elisa, Shay, Jerry W., Vialaneix, Nathalie, Bortoli, Sylvie, Lagadic-Gossmann, Dominique, Huc, Laurence, Toxicité environnementale, cibles thérapeutiques, signalisation cellulaire (T3S - UMR_S 1124), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), EHESP-Irset (EHESP-Irset), École des Hautes Études en Santé Publique [EHESP] (EHESP), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biosit : biologie, santé, innovation technologique (SFR UMS CNRS 3480 - INSERM 018), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Bina Nusantara University [Jakarta], University of Texas Southwestern Medical Center [Dallas], Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), and Vialaneix, Nathalie

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.AP] Statistics [stat]/Applications [stat.AP], digestive system diseases
Abstract

International audience; Colorectal cancer (CRC) is the fourth most common cause of death from cancer worldwide. CRC is a multistep and progressive disease where genetic factors are important in the initiation, the development and the progression of the disease. Then, CRCs can arise from sequential steps including the acquisition of mutations in the adenomatous polyposis coli (APC), followed by the mutational activation of oncogene KRAS and the inactivation of the tumor suppressor gene, TP53. The occurrence of colorectal cancer is largely influenced by the environment, including food contaminants, lifestyle and nutrition. However, the influence of mutations on the response to environmental pollutants is poorly evaluated. Environmental carcinogenesis lacks robust models to explore the interaction between genes and environment and to determine whether genetic mutations associated with colon carcinogenesis generate a particular susceptibility to the harmful effects of pollutants.

Published
2021

21. Plasmatic and ruminal metabolomes of lambs divergently selected on residual feed intake

Author

Touitou, Florian, Tortereau, Flavie, Marty-Gasset, Nathalie, Vialaneix, Nathalie, Lefort, Gaëlle, Meynadier, Annabelle, and tortereau, flavie

Subjects
[SDV] Life Sciences [q-bio]
Abstract

In ruminants, feed efficiency is a complex trait involving the host and its ruminal microbiota. In meat sheep, little is known about their joint action on animal efficiency. The objective of this work was to explore the ruminal and plasmatic metabolome differences between genetically divergent lines on residual feed intake (RFI). Rumen and blood samples were taken from male lambs belonging to the two divergent lines selected on RFI estimated under a 100% concentrate diet. Samples were taken twice: firstly when lambs were around 3 month-old and fed the same diet as their ancestors did during selection and secondly when lambs were 7-9 month-old and fed a diet with 1/3 concentrate and 2/3 dactylis hay. Rumen samples were taken using an oesophageal probe and blood samples were taken at the jugular vein before being centrifuged to separate plasma from blood cells. All the samples were frozen. Preparation of the samples consisted in several centrifugations and addition of a phosphate buffer. Samples were then analysed using NMR spectrometry and integrated with the ASICS R package. Data was then analysed both using a mixed linear model using the sasLM R packageand multivariate analysis using mixOmics R package (PLS and PLS-DA). Because of the great effect of diet, concentrate and forage based data must be separately analysed. Rumen samples did not present many differences between lines under the concentrate diet and under the forage diet, no metabolite was differently expressed. Multivariate analysis on rumen samples did not separate the two lines well. On the contrary, plasma samples showed interesting differences between the 2 lines with amino acids being (under both diets) more present in plasma samples from less efficient animalsthan in samples from efficient ones. In both diets, plasma samples from efficient lambs had significantly (P

Published
2021

22. Étude de la maturité des porcelets en fin de gestation par une approche métabolomique multifluide

Author

Lefort, Gaëlle, Vialaneix, Nathalie, Quesnel, Hélène, Pere, Marie-Christine, Billon, Yvon, Canario, Laurianne, Iannuccelli, Nathalie, Canlet, Cécile, Paris, Alain, Servien, Rémi, Liaubet, Laurence, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), UE 1372 Génétique, Expérimentation et Système Innovants, Institut National de la Recherche Agronomique (INRA)-Génétique animale (G.A.)-Physiologie Animale et Systèmes d'Elevage (PHASE), Institut National de la Recherche Agronomique (INRA)-Génétique, Expérimentation et Système Innovants (GenESI), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), MCAM, Muséum National d’Histoire Naturelle (MNHN), Centre National de la Recherche Scientifique (CNRS), Innovations Thérapeutiques et Résistances (InTheRes), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), ANR-16-CONV-0004, ANR-09-GENM005, Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité Expérimentale Elevages Porcins Innovants (GenESI), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Molécules de Communication et Adaptation des Micro-organismes (MCAM), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]

Subjects
nuclear magnetic resonance, gestation, métabolite, [SDV]Life Sciences [q-bio], metabolite, résonance magnétique nucléaire, sucking pig, génotype, porcelet, mortality, mortalité
Abstract

International audience; Selection for prolificacy and lean growth in swine has been associated with a substantial increase in piglet mortality. The first 24-48 hours after birth represent the most critical period. A major determinant for early survival is piglet maturity at birth, which relies strongly on maturation during the last month of gestation. The objective of the current study was to compare progeny from Large White and Meishan sows to describe the metabolic status of their fetuses. These two breeds differ in piglet survival. Thirty-nine sows were inseminated with mixed semen and anesthetized at 90 or 110 days after conception (mean gestation: 114 days). Their fetuses (mean = 15.7 per sow) were quickly removed by caesarean section, and this study focused on plasmatic, urinary and amniotic liquid metabolomes (1H-NMR) of 507-604 fetuses. Raw 1D Bruker spectral data files were treated with ASICS, an R package available on Bioconductor. ASICS allowed for direct identification and quantification of ca. 90 metabolites from a library of 190 metabolites, with 63-68 metabolites per fluid, of which 39 were common to the three fluids. Mixed models were applied to the quantification results to explain differences between gestational stages and fetal genotypes. A total of 55 metabolites for plasma, 37 for urine and 46 for amniotic liquid were found to be differential (FDR < 0.05). The results provide new insights into biological pathways involved in piglet maturation. In particular, we found large differences in metabolite concentrations between the two stages of gestation and betweenthe fetal genotypes for certain amino acids and sugars in plasma.; La sélection pour la prolificité chez le porc a été associée à une augmentation importante de la mortalité périnatale. Les premières 24-48 heures après la naissance représentent la période la plus critique. Un facteur déterminant pour la survie est la maturité à la naissance, très dépendante du développement foetal en fin de gestation.Notre objectif est ici de comparer le statut métabolique de porcelets descendant de Large White et Meishan qui sont deux races qui diffèrent en terme de survie et de vitalité néonatales. Trente-neuf truies ont été anesthésiées à 90 et 110 jours de gestation. Leurs foetus ont été extraits par césarienne et cette étude s'est concentrée sur les métabolomes plasmatique, urinaire et du liquide amniotique de 610 foetus. Les spectres de RMN 1H ont été analysés avec le package R ASICS qui permet d'identifier et de quantifier les métabolites présents dans un spectre. Environ 60 métabolites par fluide ont été identifiés et 39 sont communs aux trois métabolomes. Une analyse statistique multivariée et des modèles mixtes ont été réalisés pour identifier les différences significatives entre les stades gestationnels et les races. Les résultats permettent de mettre en évidence les voies métaboliques impliquées dans la maturité des porcelets : à 90 jours de gestation les métabolites nécessaires à la prolifération cellulaire sont plus abondants qu'à 110 jours où ce sont les métabolites impliqués dans le métabolisme énergétique qui sont les plus abondants. De plus, des métabolites associés à ces voies comme le myo-inositol ou le glucose, ont des concentrations significativement différentes entre les deux races ce qui tendrait à expliquer des différences de maturité.

Published
2020

25. The maturity in fetal pigs using a multi-fluid metabolomic approach

Author

Lefort, Gaëlle, primary, Servien, Rémi, additional, Quesnel, Hélène, additional, Billon, Yvon, additional, Canario, Laurianne, additional, Iannuccelli, Nathalie, additional, Canlet, Cécile, additional, Paris, Alain, additional, Vialaneix, Nathalie, additional, and Liaubet, Laurence, additional

Published
2020
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27. ASICS: a new R package for identification and quantification of metabolites in complex 1H NMR spectra

Author

Lefort, Gaëlle, Liaubet, Laurence, Canlet, Cécile, Quesnel, Hélène, Vialaneix, Nathalie, Servien, Rémi, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Innovations Thérapeutiques et Résistances (InTheRes), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), ANR-16-CONV-0004, ANR-09-GENM005, Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

National audience; 1H Nuclear Magnetic Resonance (NMR) is a high-throughput technology that allows to obtain metabolomic profile from easy-to-obtain fluids at moderate cost. It is a promising tool to detect practically usable biomarkers. However, its interpretation can be hard to make, because metabolites present from the spectrum of a complex mixture are often identified and quantified only from expert knowledge. To facilitate the use of such data, we developed a new R package, ASICS, that implements a method for the automatic identification and quantification of metabolites in 1H NMR spectra. The package combines all the steps of the analysis (management of a reference library, preprocessing, quantification, diagnosis tools to assess the quality of the quantification, post-quantification statistical analyses). To assess the performance of ASICS, both the quantification and its impact on a post-quantification differential analysis were evaluated. Correlations between ASICS relative quantifications and biochemical dosages were computed and a similar analysis was performed with other quantification methods. These comparisons showed that ASICS allows for a faster and simpler direct biological interpretation than the classical bucket approach and obtains more precisely identified and quantified metabolites than other quantification methods. ASICS is released as an R/Bioconductor package.

Published
2019

28. ASICS: identification and quantification of metabolites in complex 1D 1H NMR spectra

Author

Lefort, Gaëlle, Liaubet, Laurence, Quesnel, Hélène, Canlet, Cécile, Vialaneix, Nathalie, Servien, Rémi, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovations Thérapeutiques et Résistances (InTheRes), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; 1H Nuclear Magnetic Resonance (NMR) is a high-throughput technology that allows to obtain metabolomic profile from easy-to-obtain fluids (such as blood) at moderate cost. It is thus a promising tool to detect practically usable biomarkers. However, its interpretation can be hard to make, because metabolites present from the 1H NMR spectrum of a complex mixture are often identified and quantified only from expert knowledge. To facilitate the us e of such data, we developed a new R package, ASICS, that implements a method for the automatic identification and quantification of metabolites in 1H NMR spectra. The package combines all the steps of the analysis (management of a reference library with pure metabolite spectra, preprocessing, quantification, diagnosis tools to assess the quality of the quantification, post-quantification statistical analyses). The latest developments allow to improve alignment algorithm and quantification using information coming from all spectra of the same study. To assess the performance of ASICS, data from PORCINET (ANR-09-GENM-005) were used. Both the quantification and its impact on a post-quantification differential analysis were evaluated. Correlations between ASICS relative quantifications and biochemical dosages of three metabolites were computed a nd a similar analysis was performed with other quantification methods like Autofit or batman. These comparisons showed that ASICS allows for a faster and simpler direct biological interpretation than the classical bucket approach and obtains more precisely identified and quantified metabolites than other quantification methods. ASICS is released as an R/Bioconductor package.

Published
2019

29. Study of fetal pig maturity in relation with neonatal survival using a multi-fluids metabolomic approach

Author

Lefort, Gaëlle, Vialaneix, Nathalie, Quesnel, Hélène, Pere, Marie-Christine, Iannuccelli, Nathalie, Canlet, Cécile, Paris, Alain, Servien, Rémi, Liaubet, Laurence, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Innovations Thérapeutiques et Résistances (InTheRes), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), ANR-09-GENM-0005,PORCINET,Approche intégrée de la maturité des porcelets(2009), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; Selection for prolificacy and lean growth in swine has been associated with a substantial increase in piglet mortality. The first 24-48 hours after birth represent the most critical period. A major determinant for early survival is piglet maturity at birth, which strongly relies on the process of maturation during the last month of gestation. The objective of the current study was to compare progeny from Large White and Meishan pigs, which differ for piglet survival and vitality, to describe the metabolic status of the piglets. Thirty-nine sows were anesthetized at 90 or 110 days after conception (average gestation term: 114 days). Their fetuses (on average 15.7 per sow) were quickly obtained by caesarean section and this study focuses on the plasmatic, the urinary and the amniotic liquid metabolomes (1H-NMR) of 507 to 604 fetuses. Raw 1D Bruker spectral data files were treated with ASICS, an R package available on Bioconductor. ASICS allowed the direct identification and quantification of about 90 metabolites from a library of 190 metabolites, with about 60 metabolites per fluid, among which 39 were in common in the three fluids. A multivariate analysis and a mixed model were applied to the quantification results to explain the differences between the gestational stages and the breeds. The results provide new insights in the biological pathways involved in piglet maturation. Grants: ANR ANR-09-GENM005 (PORCINET) and ANR-16-CONV-0004 (#DigitAg), INRA (divisions GA, SA, MIA).

Published
2019

30. ASICS: a new R package for identification and quantification of metabolites in complex 1D 1H NMR spectra

Author

Lefort, Gaëlle, Liaubet, Laurence, Canlet, Cécile, Vialaneix, Nathalie, Servien, Rémi, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovations Thérapeutiques et Résistances (InTheRes), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), ANR-09-GENM005, Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, [SDV]Life Sciences [q-bio], [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; 1H Nuclear Magnetic Resonance (NMR) is a high-throughput technology that allows to obtain metabolomic profile from easy-to-obtain fluids (such as blood) at moderate cost. It is thus a promising tool to detect practically usable biomarkers. However, its interpretation can be hard to make, because metabolites present from the 1H NMR spectrum of a complex mixture are not easily identified and quantified. To facilitate the use of such data, we developed a new R package, ASICS, that implements a method for automatic identification and quantification of metabolites in 1H NMR spectra. The package combines all the steps of the analysis (management of a reference library with pure metabolite spectra, preprocessing, quantification, diagnosis tools to assess the quality of the quantification, post-quantification statistical analyses). To assess the performance of ASICS, data from PORCINET (ANR-09-GENM-005) were used. Both the quantification and its impact on a post-quantification differential analysis were evaluated. First, correlations between ASICS relative quantifications and biochemical dosages of three metabolites were computed and a similar analysis was performed with other quantification methods like Autofit or batman. Second, a differential standard approach (based on a bucket analysis and a manual expert identification) was compared with the same analysis based on ASICS quantifications. These comparisons showed that ASICS allows for a faster and simpler direct biological interpretation than the classical bucket approach and obtains more precisely identified and quantified metabolites than other quantification methods. ASICS is released as a Bioconductor package and is also available as a GALAXY module.

Published
2018

31. The effect of divergent selection on adrenocortical activity in Large White pigs on gene expression after ACTH, LPS and social stress challenges

Author

Terenina, Elena, Dugué, Claire, Kulikova, E., Bazovkina, Darya, Gress, Laure, Lefort, Gaëlle, Billon, Yvon, Larzul, Catherine, Mormède, Pierre, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Russian Academy of Sciences [Moscow] (RAS), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), and Génétique, Expérimentation et Système Innovants (GenESI)

Subjects
pig, [SDV.GEN]Life Sciences [q-bio]/Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, behavior, [SDV]Life Sciences [q-bio], selection, genetic, [INFO]Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; The effect of divergent selection on adrenocortical activity in Large White pigs on gene expression after ACTH, LPS and social stress challenges. E. Terenina1, C. Dugué1, E. Kulikova2, D. Bazovkina2, L. Gress1, G. Lefort1, Y. Billon3, C. Larzul1 & P. Mormede1 1 GenPhySE, INRA, INPT, ENSAT, Université de Toulouse, 31326 Castanet-Tolosan, France elena.mormede@inra.fr (Corresponding Author) 2 Department of Behavioral Neurogenomics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia 3 GenESI, INRA, Le Magneraud, 17700 Surgères, France The objective of the present experiment is to analyze the expression of candidate genes related to HPA axis and stress in the third generation of selection of two lines of pigs divergently selected on the basis of plasma cortisol levels measured one hour after injection of ACTH. This test allows to objectify the activity level of the hypothalamic-pituitary-adrenal (HPA) axis, a major neuroendocrine system of the stress response. The kinetics of the blood transcriptomic responses to adrenocorticotropic hormone (ACTH), lipopolysaccharide (LPS) and social stress were measured at 4 time points (0, 1h, 4h, and 24h after the challenge). Our results suggest that the responses to these different challenges are not affected in the same way by HPA axis activity. We found 22 differentially expressed genes for the inflammatory stress, 2 genes specific to the response to ACTH injection and 12 genes for the social stress. The expression of studied candidate genes did not differ according to the line. Four genes (MEGF9, S100A9, CERS4 and PSAP) were found to be differentially expressed in the three tests and could therefore be used for the overall study of stress response. With the financial support of the Agence Nationale de la Recherche, program ANR BIOADAPT, project SUSoSTRESS (ANR-12-ADAP-0008).

Published
2018

32. ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

Author

Lefort, Gaëlle, primary, Liaubet, Laurence, additional, Canlet, Cécile, additional, Tardivel, Patrick, additional, Père, Marie-Christine, additional, Quesnel, Hélène, additional, Paris, Alain, additional, Iannuccelli, Nathalie, additional, Vialaneix, Nathalie, additional, and Servien, Rémi, additional

Published
2019
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34. Analyse du méthylome chez l'embryon de poulet par séquençage haut-débit

Author

Mersch, Marjorie, Noirot, Celine, Leroux, Sophie, Esquerre, Diane, Gourichon, David, Lefort, Gaëlle, Salin, Gerald, Djebali Quelen, Sarah, Frésard, Laure, Foissac, Sylvain, Pitel, Frederique, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT], Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), GeT PlaGe, Genotoul, Pôle d'Expérimentation Avicole de Tours (PEAT), Department of Pathology, University of Alabama at Birmingham [ Birmingham] (UAB), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, ProdInra, Archive Ouverte, Génome et Transcriptome - Plateforme Génomique (GeT-PlaGe), Institut National de la Recherche Agronomique (INRA)-Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Pôle d'Expérimentation Avicole de Tours (UE PEAT), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), GenPhySE - UMR 1388 ( Génétique Physiologie et Systèmes d'Elevage ), Institut National de la Recherche Agronomique ( INRA ) -École nationale supérieure agronomique de Toulouse [ENSAT]-ENVT, Unité de Mathématiques et Informatique Appliquées de Toulouse ( MIAT INRA ), Institut National de la Recherche Agronomique ( INRA ), Pôle d'Expérimentation Avicole de Tours ( PEAT ), The University of Alabama at Birmingham [ Birmingham] ( UAB ), and Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse)

Subjects
[SDV] Life Sciences [q-bio], épigénétique, [ SDV ] Life Sciences [q-bio], méthylome, génome, [SDV]Life Sciences [q-bio], séqençage, poulet, génétique, environnement
Abstract

Investigating the molecular causes of phenotypic variations observed in farmed species usually involves thestudy of the variability in the DNA sequence. Recently, many studies have described the important role of theenvironment in the regulation of gene expression, particularly through epigenetic mechanisms. In order tocharacterize one of these epigenetic marks in chicken, DNA methylation, we have used 8 embryos (4 males and4 females) from the reciprocal cross of two lines. We developed a bioinformatic analysis pipeline to analyzemethylation marks from high-throughput sequencing. We have observed a lower methylation rate in thepromoter regions than within the genes and intergenic regions. Thanks to the transcriptomic data also availableon these embryos, we have shown that the gene expression level was higher when the promoter regions werepoorly methylated, and vice versa. The possible interaction of the genetic background or sex with themethylation level and the link with expression level in these 8 embryos is being analyzed. These analyses will beextended as part of the ANR ChickStress project, which studies the methylome response to changes intemperature and the use of a low-energy diet in chicken., L'étude des causes moléculaires des variations phénotypiques observées chez les espèces d’élevage passegénéralement par l'analyse de la variabilité de la séquence d’ADN. Récemment, de nombreuses études décriventle rôle important de l’environnement dans la régulation de l’expression des gènes, notamment via desphénomènes épigénétiques. Afin de caractériser une des marques épigénétiques chez le poulet, la méthylation del'ADN, un dispositif de 8 embryons (4 mâles et 4 femelles) issus du croisement réciproque de deux lignées a étéutilisé. À partir d'une technique d'analyse par séquençage à haut-débit des marques de méthylation, un pipelined’analyse bioinformatique a été développé. Un taux de méthylation plus faible dans les régions promotrices quedans les régions géniques et intergéniques a été observé. Grâce aux données transcriptomiques égalementdisponibles sur ces embryons, nous avons confirmé que le niveau d’expression des gènes est plus fort quand lesrégions promotrices sont peu méthylées, et inversement. La possible interaction du fond génétique ou du sexeavec le niveau de méthylation, et son lien avec les niveaux d’expression chez les 8 embryons est en coursd’analyse. Ces analyses vont être étendues dans le cadre du projet ANR ChickStress qui étudie la réponse duméthylome à des variations de température et à l’utilisation d’un aliment de faible valeur énergétique chez lapoule.

Published
2017

35. ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

Author

Lefort, Gaëlle, primary, Liaubet, Laurence, additional, Canlet, Cécile, additional, Tardivel, Patrick, additional, Pére, Marie-Christine, additional, Quesnel, Hélène, additional, Paris, Alain, additional, Iannuccelli, Nathalie, additional, Vialaneix, Nathalie, additional, and Servien, Rémi, additional

Published
2018
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36. ASICS: Automatic Statistical Identification in Complex Spectra (R package, available on Bioconductor)

Author

Lefort, Gaëlle, Servien, Rémi, Tardivel, Patrick, Vialaneix, Nathalie, and ProdInra, Migration

Subjects
[SDV] Life Sciences [q-bio], Quantitative Biology::Quantitative Methods, Quantitative Biology::Tissues and Organs, Quantitative Biology::Molecular Networks, 1H NMR, [MATH] Mathematics [math], [INFO] Computer Science [cs], metabolomics, quantification
Abstract

With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017).

Published
2017

37. Clustering of temporal sport training curves: a comparison of FDA and LDA approaches

Author

Lefort, Gaëlle, Avalos, Marta, Soret, Perrine, David, Pyne, Toussaint, Jean-François, Hellard, Philippe, Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux (UB), Australian Institute of Sport, Institut national du sport, de l'expertise et de la performance (INSEP), Institut de recherche biomédicale et d’épidémiologie du sport (IRMES - EA 7329), Université Paris Descartes - Paris 5 (UPD5)-Institut national du sport, de l'expertise et de la performance (INSEP), Université Paris Descartes - Paris 5 (UPD5), Département d'études et recherches, Fédération Française de Natation (FFN), This research was partially funded by the French Institute of Sport, Expertise and Performance (INSEP) under grant no14r21., Ecole Nationale de la Statistique et de l'Analyse de l'Information ( ENSAI ), Ensai, Ecole Nationale de la Statistique et de l'Analyse de l'Information, Statistics In System biology and Translational Medicine ( SISTM ), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Université de Bordeaux ( UB ), Institut national du sport, de l'expertise et de la performance ( INSEP ), Institut de recherche biomédicale et d’épidémiologie du sport ( IRMES - EA 7329 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut national du sport, de l'expertise et de la performance ( INSEP ), Université Paris Descartes - Paris 5 ( UPD5 ), Fédération Française de Natation ( FFN ), Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Epidémiologie et Biostatistique [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, [ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG], Functional data analysis, [ STAT.ME ] Statistics [stat]/Methodology [stat.ME], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Longitudinal data analysis, Sport science data, [STAT.CO]Statistics [stat]/Computation [stat.CO], [STAT.ME]Statistics [stat]/Methodology [stat.ME], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], [ STAT.CO ] Statistics [stat]/Computation [stat.CO]
Abstract

International audience; Functional data analysis (FDA) and longitudinal data analysis (LDA) are the main approaches to analyze repeated measures data (in which multiple measurements are made on the same subject across time). Typically, FDA is applied when the data are dense, assumed to be observed in the continuum, and without noise. LDA is usually applied when data are sparse, possibly with different number of measurements across individuals, and subject to error.In elite sport, the parameters of the training program (intensity, volume, frequency, distribution and duration of high-intensity, recovery, and competition periods) should be manipulated systematically to optimize performance and reduce the risk of injury. Sport training data are recorded densely over time. However, measurements, such as duration of follow-up or duration of the season, vary among subjects. Subject-specific variations may introduce substantial measurement error.The statistical objectives of this study were the following:- First, to review the literature on the most commonly used methods for clustering of time evolution curves with a publicly available R code.- Second, to implement FDA and LDA methods presenting publicly available R code: k-means based on the standard Euclidian distance, a discrete Frèchet distance, and a distance of functions; Gaussian mixture model - based clustering for standard, longitudinal and functional data; and latent class mixed models.- Third, using data from a twenty - year longitudinal study of training practices of elite athletes, to perform a clustering analysis using relevant methods.- Fourth, to compare the results and interpret them. Comparison criteria were mainly based on computational and practical aspects.The practical goal of this project was to identify training profiles and to characterize them to provide relevant tools for supporting decision-making in monitoring athletes' training programs.

Published
2016

38. A comparison of unsupervised curve classification methods for sport training data

Author

Lefort, Gaëlle, Avalos, Marta, Soret, Perrine, David, Pyne, Toussaint, Jean-François, Hellard, Philippe, Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] (ENSAI), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux (UB), Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Australian Institute of Sport, Institut national du sport, de l'expertise et de la performance (INSEP), Institut de recherche biomédicale et d’épidémiologie du sport (IRMES - EA 7329), Université Paris Descartes - Paris 5 (UPD5)-Institut national du sport, de l'expertise et de la performance (INSEP), Université Paris Descartes - Paris 5 (UPD5), Fédération Française de Natation (FFN), This research was partially funded by the French Institute of Sport, Expertise and Performance (INSEP) under grant no14r21., The International Society for NonParametric Statistics (ISNPS), Ecole Nationale de la Statistique et de l'Analyse de l'Information ( ENSAI ), Ensai, Ecole Nationale de la Statistique et de l'Analyse de l'Information, Statistics In System biology and Translational Medicine ( SISTM ), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Université de Bordeaux ( UB ), Institut national du sport, de l'expertise et de la performance ( INSEP ), Institut de recherche biomédicale et d’épidémiologie du sport ( IRMES - EA 7329 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut national du sport, de l'expertise et de la performance ( INSEP ), Université Paris Descartes - Paris 5 ( UPD5 ), Fédération Française de Natation ( FFN ), Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Avalos, Marta

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ME] Statistics [stat]/Methodology [stat.ME], [ STAT.AP ] Statistics [stat]/Applications [stat.AP], [ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [STAT.CO] Statistics [stat]/Computation [stat.CO], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG], Functional data analysis, [ STAT.ME ] Statistics [stat]/Methodology [stat.ME], [STAT.AP] Statistics [stat]/Applications [stat.AP], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Longitudinal data analysis, Sport science data, [STAT.CO]Statistics [stat]/Computation [stat.CO], [STAT.ME]Statistics [stat]/Methodology [stat.ME], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], [ STAT.CO ] Statistics [stat]/Computation [stat.CO]
Abstract

International audience; Achieving peak performance at a specified time is the primary goal of athletes’ training programs. To optimize performance and reduce the risk of injury, a comprehensive list of training program parameters (e.g. intensity, volume, frequency, distribution, duration and type) requires careful management. This work focuses on clustering of time evolution curves of training measurements.Training data are recorded densely over time. However, duration of follow-up and duration of the seasons vary among subjects. Also, subject-specific variation can induce substantial error. Functional data analysis (FDA) and longitudinal data analysis (LDA) are the main approaches to analyze repeated measures data (in which multiple measurements are made on the same subject across time). Typically, FDA is applied when the data are dense, assumed to be observed in the continuum, and a function of time. LDA is usually applied when data are sparse, possibly with different number of measurements across individuals, and subject to error. We compared several FDA and LDA methods implemented through publicly available R code: k-means based on the standard Euclidian distance, a discrete Fréchet distance [2], and a functional distance [1]; Gaussian mixture model–based clustering for standard [4], longitudinal [5] and functional [3] data; and latent class mixed models [6]. We discuss advantages and limitations including computational and practical aspects.References[1] Febrero-Bande, M. and Oviedo de la Fuente, M. (2012). Statistical computing in functional data analysis: the R package fda.usc. Journal of Statistical Software, 51, 1–28.[2] Genolini, C. and Falissard, B. (2011). Kml : A package to cluster longitudinal data. Computer Methods and Programs in Biomedicine.[3] Jacques, J. and Preda, C. (2013). Funclust: A curves clustering method using functional random variables density approximation. Neurocomputing, 112, 164–171.[4] Lebret, R., Iovleff, S., Langrognet, F., Biernacki, C., Celeux, G., and Govaert, G. (2014). Rmixmod: The R package of the model–based unsupervised, supervised and semi–supervised classification mixmod library. Journal of Statistical Software.[5] McNicholas, P. D. and Murphy, T. B. (2010). Model–based clustering of longitudinal data. Canadian Journal of Statistics, 38, 153–168.[6] Proust-Lima, C., Philipps, V., and Liquet, B. (2015). Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. Technical report, University of Bordeaux. arXiv:1503.00890v2.

Published
2016

39. Workflow-BS an integrative workflow for RRBS and WGBS data. From the BS-seq to the DMR

Author

Foissac, Sylvain, Lefort, Gaëlle, Mersch, Marjorie, Pitel, Frederique, Noirot, Celine, Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT], Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), ProdInra, Archive Ouverte, École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
[SDV] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], informatic, integrative workflow
Abstract

Workflow-BS an integrative workflow for RRBS and WGBS data. From the BS-seq to the DMR. NGS'16 Genome Annotation

Published
2016

40. ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra.

Author

Lefort, Gaëlle, Liaubet, Laurence, Canlet, Cécile, Tardivel, Patrick, Père, Marie-Christine, Quesnel, Hélène, Paris, Alain, Iannuccelli, Nathalie, Vialaneix, Nathalie, and Servien, Rémi

Subjects
*NUCLEAR magnetic resonance spectroscopy, *WORKFLOW, *NUCLEAR magnetic resonance, *METABOLITE analysis, *AUTOMATIC identification, *WORKFLOW management, *STATISTICS
Abstract

Motivation In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. Availability and implementation ASICS is distributed as an R package, available on Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2019
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41. ASICS: an automatic method for identification and quantification of metabolites in complex 1D H NMR spectra.

Author

Tardivel, Patrick, Canlet, Cécile, Lefort, Gaëlle, Tremblay-Franco, Marie, Debrauwer, Laurent, Concordet, Didier, and Servien, Rémi

Subjects
METABOLITE analysis, METABOLOMICS, PROTON magnetic resonance, NUCLEAR magnetic resonance, MASS spectrometry
Abstract

Introduction: Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features. Objectives: In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra. Methods: A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues. Results: The performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods. Conclusion: ASICS is a completely automated procedure to identify and quantify metabolites in H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles. [ABSTRACT FROM AUTHOR]

Published
2017
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44. ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

Author

Hélène Quesnel, Cécile Canlet, Gaëlle Lefort, Rémi Servien, Marie-Christine Père, Patrick Tardivel, Nathalie Vialaneix, Alain Paris, Laurence Liaubet, Nathalie Iannuccelli, Lefort, Gaëlle, Vialaneix, Nathalie, Servien, Remi, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Analyse de Xénobiotiques, Identification, Métabolisme (E20 Metatoul-AXIOM), ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Wroclaw, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Molécules de Communication et Adaptation des Micro-organismes (MCAM), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Innovations Thérapeutiques et Résistances (InTheRes), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), ANR-09-GENM-0005,PORCINET,Approche intégrée de la maturité des porcelets(2009), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-MetaToul-MetaboHUB, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
Statistics and Probability, Magnetic Resonance Spectroscopy, Computer science, Systems biology, Proton Magnetic Resonance Spectroscopy, [SDV]Life Sciences [q-bio], computer.software_genre, 01 natural sciences, Biochemistry, Workflow, Bioconductor, 03 medical and health sciences, Metabolomics, [INFO]Computer Science [cs], [MATH]Mathematics [math], Molecular Biology, 030304 developmental biology, 0303 health sciences, [SDV.GEN]Life Sciences [q-bio]/Genetics, 010401 analytical chemistry, Magnetic Resonance Imaging, 0104 chemical sciences, Computer Science Applications, Computational Mathematics, R package, Metabolic pathway, Identification (information), [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, Computational Theory and Mathematics, Proton NMR, Biomarker (medicine), Data mining, computer, Software
Abstract

Motivation In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition. Availability and implementation ASICS is distributed as an R package, available on Bioconductor. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2019
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45. Deciphering autism heterogeneity: a molecular stratification approach in four mouse models.

Author

Gora C, Dudas A, Vaugrente O, Drobecq L, Pecnard E, Lefort G, and Pellissier LP

Subjects
Animals, Mice, Male, Mice, Inbred C57BL, Early Growth Response Protein 1 genetics, Early Growth Response Protein 1 metabolism, Brain metabolism, Behavior, Animal, Forkhead Transcription Factors genetics, Homer Scaffolding Proteins genetics, Homer Scaffolding Proteins metabolism, Autistic Disorder genetics, Oxytocin genetics, Disease Models, Animal, Autism Spectrum Disorder genetics, Receptors, Oxytocin genetics
Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction and communication, as well as restrained or stereotyped behaviors. The inherent heterogeneity within the autism spectrum poses challenges for developing effective pharmacological treatments targeting core features. Successful clinical trials require the identification of robust markers to enable patient stratification. In this study, we identified molecular markers within the oxytocin and immediate early gene families across five interconnected brain structures of the social circuit. We used wild-type and four heterogeneous mouse models, each exhibiting unique autism-like behaviors modeling the autism spectrum. While dysregulations in the oxytocin family were model-specific, immediate early genes displayed widespread alterations, reflecting global changes across the four models. Through integrative analysis, we identified Egr1, Foxp1, Homer1a, Oxt, and Oxtr as five robust and discriminant molecular markers that allowed the successful stratification of the four models. Importantly, our stratification demonstrated predictive values when challenged with a fifth mouse model or identifying subgroups of mice potentially responsive to oxytocin treatment. Beyond providing insights into oxytocin and immediate early gene mRNA dynamics, this proof-of-concept study represents a significant step toward the potential stratification of individuals with ASD. This work has implications for the success of clinical trials and the development of personalized medicine in autism., (© 2024. The Author(s).)

Published
2024
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46. Joint Automatic Metabolite Identification and Quantification of a Set of 1 H NMR Spectra.

Author

Lefort G, Liaubet L, Marty-Gasset N, Canlet C, Vialaneix N, and Servien R

Subjects
Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Proton Magnetic Resonance Spectroscopy, Metabolome, Metabolomics
Abstract

Metabolomics is a promising approach to characterize phenotypes or to identify biomarkers. It is also easily accessible through NMR, which can provide a comprehensive understanding of the metabolome of any living organisms. However, the analysis of 1 H NMR spectrum remains difficult, mainly due to the different problems encountered to perform automatic identification and quantification of metabolites in a reproducible way. In addition, methods that perform automatic identification and quantification of metabolites are often designed to process one given complex mixture spectrum at a time. Hence, when a set of complex mixture spectra coming from the same experiment has to be processed, the approach is simply repeated independently for every spectrum, despite their resemblance. Here, we present new methods that are the first to either align spectra or to identify and quantify metabolites by integrating information coming from several complex spectra of the same experiment. The performances of these new methods are then evaluated on both simulated and real datasets. The results show an improvement in the metabolite identification and in the accuracy of metabolite quantifications, especially when the concentration is low. This joint procedure is available in version 2.0 of ASICS package.

Published
2021
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